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DTSTAMP:20240214T070243Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T171000
DTEND;TZID=Australia/Melbourne:20231212T172000
UID:siggraphasia_SIGGRAPH Asia 2023_sess142_papers_446@linklings.com
SUMMARY:Input-Dependent Uncorrelated Weighting for Monte Carlo Denoising
DESCRIPTION:Technical Communications, Technical Papers\n\nJonghee Back (Gw
 angju Institute of Science and Technology), Binh-Son Hua (Trinity College 
 Dublin), Toshiya Hachisuka (University of Waterloo), and Bochang Moon (Gwa
 ngju Institute of Science and Technology)\n\nImage-space denoising techniq
 ues have been widely employed in Monte Carlo rendering, typically blending
  neighboring pixel estimates using a denoising kernel. It is widely recogn
 ized that a kernel should be adapted to characteristics of the input pixel
  estimates in order to ensure robustness to diverse image features and amo
 unt of noise. Denoising with such an input-dependent kernel, however, can 
 introduce a bias that makes the denoised estimate even less accurate than 
 the noisy input estimate. Consequently, it has been considered essential t
 o balance the bias introduced by denoising and the reduction of noise. We 
 propose a new framework to define an input-dependent kernel that departs f
 rom the existing approaches based on error estimation or supervised learni
 ng. Rather than seeking an optimal bias-noise balance as in those existing
  approaches, we propose to constrain the amount of bias introduced by deno
 ising. Such a constraint is made possible by the concept of uncorrelated s
 tatistics, which has never been applied for denoising. By designing an inp
 ut-dependent kernel with uncorrelated weights against the input pixel esti
 mates, our denoising kernel can reduce data-dependent noise with a negligi
 ble amount of bias in most cases. We demonstrate the effectiveness of our 
 method for various scenes.\n\nRegistration Category: Full Access\n\nSessio
 n Chair: Michael Gharbi (Adobe, MIT)
URL:https://asia.siggraph.org/2023/full-program?id=papers_446&sess=sess142
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